Since AL requires a base (pre-trained) model to work [1]}, learning that model from annotated sentences sampled using sequential or random sampling can be very expensive due to the labeling starvation problem mentioned before. Therefore, we developed an Entity Set Expansion (ESE) method that incorporates and exploits the semantics of the desired entity class to more informatively sample sentences that are likely to have entities of the desired class.
